



'''simple docstring'''






import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     lowerCAmelCase:   Optional[int]					=		False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor

class 				a__(   unittest.TestCase      ):







    def __init__( self					:    Any		,     __snake_case					:    Tuple		,     __snake_case					:    int=7		,     __snake_case					:    Optional[Any]=3		,     __snake_case					:    Dict=18		,     __snake_case					:    Optional[int]=30		,     __snake_case					:    List[Any]=4_00		,     __snake_case					:    Tuple=None		,     __snake_case					:    Any=True		,     __snake_case					:    Tuple=True		,     __snake_case					:    Optional[int]=None		,     ):
      a		:   str	  =	size if size is not None else {'height': 20, 'width': 20}
      a		:   List[Any]	  =	parent
      a		:   int	  =	batch_size
      a		:   int	  =	num_channels
      a		:   Any	  =	image_size
      a		:   Any	  =	min_resolution
      a		:   Optional[Any]	  =	max_resolution
      a		:   Dict	  =	size
      a		:   Dict	  =	do_normalize
      a		:   str	  =	do_convert_rgb
      a		:   Any	  =	[5_12, 10_24, 20_48, 40_96]
      a		:   Dict	  =	patch_size if patch_size is not None else {'height': 16, 'width': 16}







    def      lowercase_			( self					:    Tuple      ):
      return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







    def      lowercase_			( self					:    Any      ):
      a		:   List[str]	  =	'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
      a		:   Tuple	  =	Image.open(requests.get(__snake_case		,     stream=__snake_case      ).raw      ).convert('RGB'      )
      return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11						,					reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""						,					)
@require_torch
@require_vision
class 				a__(   lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       PixaStructImageProcessor if is_vision_available() else None







    def      lowercase_			( self					:    Tuple      ):
      a		:   int	  =	PixaStructImageProcessingTester(self      )







    @property
    def      lowercase_			( self					:    str      ):
      return self.image_processor_tester.prepare_image_processor_dict()







    def      lowercase_			( self					:    Any      ):
      a		:   str	  =	self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(__snake_case		,     'do_normalize'      )      )
      self.assertTrue(hasattr(__snake_case		,     'do_convert_rgb'      )      )







    def      lowercase_			( self					:    str      ):
      a		:   Optional[int]	  =	self.image_processor_tester.prepare_dummy_image()

      a		:   Any	  =	self.image_processing_class(**self.image_processor_dict      )
      a		:   List[Any]	  =	20_48

      a		:   List[Any]	  =	image_processor(__snake_case		,     return_tensors='pt'		,     max_patches=__snake_case      )
      self.assertTrue(torch.allclose(inputs.flattened_patches.mean()		,     torch.tensor(0.0606      )		,     atol=1e-3		,     rtol=1e-3      )      )







    def      lowercase_			( self					:    Dict      ):
      # Initialize image_processor
      a		:   int	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      a		:   Any	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     Image.Image      )

      # Test not batched input
      a		:   Union[str, Any]	  =	(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * self.image_processor_tester.num_channels
      ) + 2

      for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        a		:   Any	  =	image_processor(
            image_inputs[0]		,     return_tensors='pt'		,     max_patches=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

        # Test batched
        a		:   str	  =	image_processor(
            __snake_case		,     return_tensors='pt'		,     max_patches=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )







    def      lowercase_			( self					:    Optional[Any]      ):
      # Initialize image_processor
      a		:   List[Any]	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      a		:   Tuple	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     Image.Image      )

      # Test not batched input
      a		:   Dict	  =	(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * self.image_processor_tester.num_channels
      ) + 2

      a		:   Union[str, Any]	  =	True

      for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        with self.assertRaises(__snake_case      ):
          a		:   int	  =	image_processor(
              image_inputs[0]		,     return_tensors='pt'		,     max_patches=__snake_case      ).flattened_patches

        a		:   Union[str, Any]	  =	'Hello'

        a		:   int	  =	image_processor(
            image_inputs[0]		,     return_tensors='pt'		,     max_patches=__snake_case		,     header_text=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

        # Test batched
        a		:   Any	  =	image_processor(
            __snake_case		,     return_tensors='pt'		,     max_patches=__snake_case		,     header_text=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )







    def      lowercase_			( self					:    Optional[int]      ):
      # Initialize image_processor
      a		:   Tuple	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      a		:   str	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case		,     numpify=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     np.ndarray      )

      a		:   int	  =	(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * self.image_processor_tester.num_channels
      ) + 2

      for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        a		:   List[str]	  =	image_processor(
            image_inputs[0]		,     return_tensors='pt'		,     max_patches=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

        # Test batched
        a		:   Any	  =	image_processor(
            __snake_case		,     return_tensors='pt'		,     max_patches=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )







    def      lowercase_			( self					:    int      ):
      # Initialize image_processor
      a		:   List[str]	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      a		:   List[Any]	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case		,     torchify=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     torch.Tensor      )

      # Test not batched input
      a		:   str	  =	(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * self.image_processor_tester.num_channels
      ) + 2

      for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        a		:   List[str]	  =	image_processor(
            image_inputs[0]		,     return_tensors='pt'		,     max_patches=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

        # Test batched
        a		:   Union[str, Any]	  =	image_processor(
            __snake_case		,     return_tensors='pt'		,     max_patches=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11						,					reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""						,					)
@require_torch
@require_vision
class 				a__(   lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       PixaStructImageProcessor if is_vision_available() else None







    def      lowercase_			( self					:    Dict      ):
      a		:   Any	  =	PixaStructImageProcessingTester(self		,     num_channels=4      )
      a		:   Union[str, Any]	  =	3







    @property
    def      lowercase_			( self					:    int      ):
      return self.image_processor_tester.prepare_image_processor_dict()







    def      lowercase_			( self					:    Tuple      ):
      a		:   Union[str, Any]	  =	self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(__snake_case		,     'do_normalize'      )      )
      self.assertTrue(hasattr(__snake_case		,     'do_convert_rgb'      )      )







    def      lowercase_			( self					:    Optional[Any]      ):
      # Initialize image_processor
      a		:   Dict	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      a		:   Optional[Any]	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     Image.Image      )

      # Test not batched input
      a		:   Optional[Any]	  =	(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * (self.image_processor_tester.num_channels - 1)
      ) + 2

      for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        a		:   Tuple	  =	image_processor(
            image_inputs[0]		,     return_tensors='pt'		,     max_patches=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

        # Test batched
        a		:   Optional[Any]	  =	image_processor(
            __snake_case		,     return_tensors='pt'		,     max_patches=__snake_case      ).flattened_patches
        self.assertEqual(
            encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )




'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    int		,     __snake_case					:    Callable		,     __snake_case					:    Optional[Features] = None		,     __snake_case					:    str = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    Optional[dict] = None		,     __snake_case					:    Optional[int] = None		,     **__snake_case					:    Optional[int]		,     ):
      super().__init__(
          features=__snake_case		,     cache_dir=__snake_case		,     keep_in_memory=__snake_case		,     streaming=__snake_case		,     num_proc=__snake_case		,     **__snake_case		,     )
      a		:   List[Any]	  =	Generator(
          cache_dir=__snake_case		,     features=__snake_case		,     generator=__snake_case		,     gen_kwargs=__snake_case		,     **__snake_case		,     )







    def      lowercase_			( self					:    Any      ):
      # Build iterable dataset
      if self.streaming:
        a		:   Optional[int]	  =	self.builder.as_streaming_dataset(split='train'      )
      # Build regular (map-style) dataset
      else:
        a		:   List[Any]	  =	None
        a		:   Any	  =	None
        a		:   Optional[int]	  =	None
        a		:   List[str]	  =	None

        self.builder.download_and_prepare(
            download_config=__snake_case		,     download_mode=__snake_case		,     verification_mode=__snake_case		,     base_path=__snake_case		,     num_proc=self.num_proc		,     )
        a		:   Dict	  =	self.builder.as_dataset(
            split='train'		,     verification_mode=__snake_case		,     in_memory=self.keep_in_memory      )
      return dataset